Nouwen, A.,
Deschenes, S.S.,
Balkhiyarova, Z.,
Albertorio-Diaz, J.R.,
Prokopenko, I.,
Schmitz, N. (2021) -affective items) models were examined.
Results:
Results demonstrated that the most stringent models, testing equal
Yanchao, L.,
Sibin, Z.,
Gareev, I.,
Huan, X.,
Junfei, Z.,
Chunyang, L.,
Beylerli, O.,
Sufianov, A.,
Chao, Y.,
Yuyan, G.,
Xun, X.,
Ahmad, A. (2022) : This study used human induced pluripotent stem cell (hiPSC)-derived monolayer brain cell
dataset and human
Song, Wanqing,
Chen, Jianxue,
Wang, Zhen,
Kudreyko, Aleksey,
Qi, Deyu,
Zio, Enrico (2023) of Maryland CALEC
dataset. Our forecasting
results demonstrate the high accuracy of the method and its
and determining the volume of inhomogeneity.
Datasets from the public database MosMedData and NSCLC were used
Lopukhova, Ekaterina A.,
Yusupov, Ernest S.,
Ibragimova, Rada R.,
Idrisova, Gulnaz M.,
Mukhamadeev, Timur R.,
Grakhova, Elizaveta P.,
Kutluyarov, Ruslan V. (2025) effectively manage issues commonly encountered with medical
datasets, such as class imbalance and strong
LIU, Z.,
ZHANG, R.,
CHEN, X.,
YAO, P.,
YAN, T.,
LIU, W.,
YAO, J.,
ZHAO, S.,
SOKHATSKII, A.,
GAREEV, I. (2019) .
Methods
The GSE24265
dataset, consisting of data from four perihematomal brain tissues and seven
Wang, Chunlei,
Beylerli, Ozal,
Gu, Yan,
Xu, Shancai,
Ji, Zhiyong,
Ilyasova, Tatiana,
Gareev, Ilgiz,
Chekhonin, Vladimir (2024) genes implicated in the development and progression of glioblastoma by analyzing microarray
datasets GSE
. Methods: The GSE24265
dataset, consisting of data from four perihematomal brain tissues and seven